code: def unique_values(
      x: paddle.Tensor, /, *, out: Optional[paddle.Tensor] = None
  ) -> paddle.Tensor:
      nan_count = paddle.sum(paddle.isnan(x))
      unique = paddle.unique(x)
      if nan_count > 0:
          nans = paddle.full(
              shape=[nan_count], fill_value=float("nan")
          ).cast(unique.dtype)
          unique = paddle.concat([unique, nans])
      return unique.cast(x.dtype)
  
file_path: ivy/ivy/functional/backends/paddle/set.py
imports: import paddle
  from typing import Tuple, Optional
  from collections import namedtuple
  import ivy.functional.backends.paddle
  from ivy.func_wrapper import with_supported_dtypes
  from .None import backend_version
label_desc: Use image node_id 4 for calling a global function with example usage: unique_values() and returns: unique